61 results about "Semi-structured data" patented technology

The invention provides a resume matching method and device, and is suitable for the technical field of data processing. The method comprises the following steps: analyzing and mining resume unstructured data, resume semi-structured data, resume structured data, post demand unstructured data, post demand semi-structured data and post demand structured data to obtain resume implicit information and post demand implicit information; according to a resume matching calculation parameter, a resume matching parameter optimization step length, resume unstructured data, resume semi-structured data, resume structured data, resume implicit information, post demand unstructured data, post demand semi-structured data, post demand structured data and post demand implicit information; and generating resume matching information. The matching accuracy of the resumes and the post requirements is effectively improved, the resume matching reliability and rationality are enhanced, and therefore the recruitment efficiency and effectiveness are improved.

The invention relates to the technical field of knowledge questions and answers, in particular to a large language model knowledge question and answer method and system fused with a multi-modal knowledge graph. The method comprises the following steps: acquiring structured and semi-structured data and unstructured multi-modal data in an enterprise business scene; capturing data change through a real-time synchronization technology and executing targeted preprocessing to ensure the consistency of data quality and modal characteristics; constructing a dynamically updated multi-modal knowledge graph based on the preprocessed data, and integrating a multi-modal embedded model, a reordering model and a fine-tuned generative large model to obtain a knowledge storage-feature matching-semantic generation integrated architecture; according to the method, the defect of knowledge lag of a traditional system is overcome, and seamless fusion of the AI capability and the enterprise core business process is realized.

The invention discloses a multi-modal medical data fusion and analysis method, which is applied to the technical field of medical informatization and comprises the following steps: acquiring multi-modal medical data; wherein the multi-modal medical data comprises structured data, unstructured data and semi-structured data generated in the diagnosis and treatment process of the patient; structural data features are extracted by adopting an optimized Z-score standardization formula; using sparse attention CNN to extract unstructured data features; deepening diagnosis and treatment semantic association by adopting a Transform encoder and entity embedding fusion form, and extracting semi-structured data features; multi-modal data are fused through closed-loop design of coding dimension reduction integration, decoding reconstruction verification, exclusive loss optimization and feature standardization output, and a feature relevance verification mechanism is fused; and matching the fused multi-modal data with a data analysis task, and performing task analysis and mining. According to the invention, efficient and reliable technical support is provided for medical digital transformation and precise diagnosis and treatment.

The invention discloses a low-code process automatic arrangement method and system, and particularly relates to the technical field of automatic arrangement, and the method comprises the steps: carrying out the unified collection and verification of an external system, resources and system boundaries before deployment, generating an operation environment fingerprint with a signature, and carrying out the strong binding of the fingerprint with a process instance; process DAG modeling is completed on the visual canvas, system and semantic double validity verification is carried out, and a post-curing parameter snapshot is used as a subsequent unique parameter source; in the online simulation period, observable measurement is collected, a deviation perception index is formed, and steady selection and switching of an execution kernel are achieved by combining hysteresis and a minimum residence strategy; in a production state, a canary release strategy, a degradation strategy and a rollback strategy are used for communicating simulation and real disk, and the SLA and the key KPI are continuously monitored; normalized collection and immutable archiving are carried out on structured and semi-structured data in the operation period, an evidence chain and a minimum reproduction experiment package are generated based on hash and signature, and the comparability and traceability of cross-version results are achieved.

A method (300) of storing semi-structured data (12U) includes receiving user data (12) comprising semi-structured user data from a user (10) of a query system (150). The method includes receiving an indication (14) that the semi-structured user data fails to include a fixed schema. In response, the method further includes parsing the semi-structured user data into a plurality of data paths (210) and extracting a data type (220) associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table (204) of a database in communication with the query system, wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

The application discloses a cardiovascular chronic disease data management method based on artificial intelligence, relates to the technical field of artificial intelligence medical management, and comprises the following steps: collecting patient multi-element heterogeneous medical data, normalizing and converting structured and semi-structured data, extracting key features of unstructured medical image data and vectorizing and encoding the key features, and generating a standardized cardiovascular chronic disease comprehensive data cube; calling a pre-trained multi-modal cardiovascular risk assessment model to analyze the data cube, and generating a comprehensive risk assessment report containing a risk level, a contribution factor, an evolution trend and an individualized early warning threshold; combining a clinical path knowledge base to generate an individualized health management plan, continuously collecting plan execution feedback data, dynamically updating the data cube, and iteratively optimizing the assessment report and the management plan. The method realizes multi-element heterogeneous data regularization, enriches risk assessment dimensions, realizes dynamic adaptation of a health management plan, and improves the standardization and individualization level of cardiovascular chronic disease data management.

The invention discloses a data warehouse construction and application method based on multi-source data fusion, and relates to the technical field of data fusion, and the method comprises the steps: multi-source data integration layer construction: obtaining structured, unstructured and semi-structured data through a heterogeneous data collection module, carrying out the automatic preprocessing and standardization processing of the data, and obtaining a multi-source data integration layer; the standardization processing comprises a unified naming rule, a coding rule and a data type; designing a core model of the data warehouse: constructing a star model based on a business theme, the star model comprising at least one fact table and a plurality of associated dimension tables, the fact table storing business index data, and the dimension tables storing descriptive attribute data; implementing an ETL automatic process: configuring a data extraction and conversion rule and a scheduling strategy through a visual ETL tool, automatically loading the data processed by the integration layer to a star model, and monitoring a task state in real time; and intelligent query and application recommendation.

The application discloses an analysis system and an analysis method for automatically detecting semi-structured data quality problems, the analysis method takes JSON data in semi-structured data as a specific research object, and comprises the following steps: a parsing mode module is used for parsing semi-structured data into an aggregated schema tree; a data quality problem monitoring module is used for automatically detecting potential data quality problems of data according to the aggregated schema tree and a data quality space; a visualization generation module is used for visualizing the aggregated schema tree and the data quality problems, helping users to interactively find and view the data quality problems; and a data cleaning module is used for user configuration and solution of the data quality problems. The system can help users to quickly and effectively locate and clean the data quality problems of semi-structured data.

This invention relates to the field of pipeline inspection and management technology, specifically a long-distance pipeline inspection and management system and method, including a centralized control center, edge processing equipment, cameras, a cloud platform, and manual inspection terminals; the cameras are installed at designated locations for real-time monitoring of target objects. The advantages of this invention are: by introducing a data governance module and adding three-dimensional tags containing timestamps, location coordinates, and associated object IDs to multi-source data, and simultaneously establishing a core index master ledger and associated ledgers bound together by unique data IDs, this system achieves automatic association and fusion of unstructured, structured, and semi-structured data generated during inspections. This solves the "data silo" problem caused by inconsistent data formats and scattered storage in existing technologies, providing a complete and traceable data chain for each potential hazard or equipment status, supporting full-process traceability and analysis.

The embodiment of the invention provides a knowledge graph construction method, a fault query method, electronic equipment and a storage medium. The knowledge graph construction method comprises the steps of obtaining a first knowledge graph of a target system, wherein the first knowledge graph is formed based on an association relationship between first entities of structured data and an association relationship between second entities of semi-structured data in the target system; extracting an association relationship between third entities corresponding to the non-structural data in the target system based on an extraction model, wherein the extraction model is an entity relationship extraction model trained based on the first entities, the association relationship between the first entities and the association relationship between the second entities; and updating the first knowledge graph based on the third entities and the association relationship between the third entities to obtain a second knowledge graph. According to the scheme of the embodiment, the knowledge graph is automatically constructed, the human input is reduced, and the manual annotation cost is saved; and a technical basis is provided for quickly positioning fault nodes in the system.

The embodiment of the invention discloses a column type partition storage method and device for variant types, equipment and a medium. A specific embodiment of the method comprises the following steps: performing grammar embedding processing on a variant type; performing data extraction processing on the mode template statement; generating mode template metadata; performing data analysis processing on the initial semi-structured data; performing data grading matching processing on the initial key value pair data; performing conversion verification processing on each unmatched key value pair data and each composite key value pair data; performing data screening processing on each composite conversion sub-column; performing partition coding processing on each unmatched sub-column, each target conversion sub-column and each to-be-processed conversion sub-column; and performing determinant partition storage on the dynamic sub-column region data, the sparse column region data and the typed sub-column region data. According to the embodiment, waste of storage resources can be reduced.

A system for interactive searches based on user queries data and a plurality of Large Language Models (LLMs) including a processor of a Human-Machine Interface (HMI) server node configured to host a network of LLMs and at least one machine learning module (ML) and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a search request input data from the at least one user-entity node; evaluate, by a first dedicated LLM, relevance of the search request input data by discerning between primary and secondary information; responsive to evaluation by the first dedicated LLM, derive classifying features from the primary and secondary information and generate a feature vector based on the classifying features; ingest the feature vector into the ML module configured to extract additional search parameters from a predictive search model based on historical search data associated with the at least one user-entity node; dissect, by a second dedicated LLM, the search request input data and the additional search parameters to separate the data into qualitative and quantitative criteria elements based on the primary and the secondary information; transform, by a third dedicated LLM, the quantitative criteria elements into structured queries for a database; process, by a fourth dedicated LLM, the qualitative criteria elements by searching through a semi-structured data repository; and synthesize, by the fifth LLM, processed search findings into a succinct human-language summary.

The application discloses a ship plate high-strength steel material composition design method based on multi-target black box optimization, relates to the field of metal material design, and collects ship plate high-strength steel historical data to obtain a standard semi-structured data set through alignment and cleaning treatment; based on the data set, optimization targets, decision variables and constraint conditions are determined, and a multi-target optimization model is constructed; a mechanical property prediction model integrating multiple performances is obtained through machine learning algorithm training; an optimal solution set is obtained through iterative calculation of a reference vector guided multi-target optimization algorithm, and a final composition design scheme is selected by combining target steel grade service scenarios and assigning weights, so that the complex mapping relationship of composition-process-performance is accurately quantified, the strength, toughness, weldability and corrosion resistance are simultaneously optimized to match service requirements, the optimization algorithm considers global exploration and local development, the optimal solution under multiple constraints is efficiently obtained, manual experience derivation is not needed, and the composition design can be popularized to other high-strength steels or special steels.

The invention relates to a knowledge graph construction method based on Bi-GRU relation extraction and a vector variance algorithm. The method comprises the following steps: constructing an initial knowledge graph hierarchical structure from structured knowledge; extracting an implicit classification relationship from the semi-structured open tag by adopting a co-word analysis algorithm; constructing a Bi-GRU remote supervision relation extraction model containing a multi-scale attention mechanism and an improved cross entropy loss function, and extracting a non-classification relation from an unstructured text; calculating node membership and variance through a vector variance algorithm, and determining a threshold to delete irrelevant nodes and associated edges in the field; the method aims at solving the technical problems that an existing domain knowledge graph depends on structured / semi-structured data, so that knowledge is incomplete, non-structured text relation extraction is interfered by noise, and domain-independent node redundancy exists in the graph.

The application discloses a kind of review expert database random extraction evaluation methods.The method relates to intelligent matching extraction technical field, including the following steps: multi-source data acquisition and vectorization processing, matching accuracy dynamic adjustment, data assetization closed loop transformation and regulation and expert random extraction execution.The application is by collecting review related data and document and carries out vectorization processing, it is to unstructured, semi-structured data into structured vector data, obtains expert and review matter matching accuracy, and judges whether to carry out dynamic adjustment, then carries out expert and review project correlation analysis.Based on correlation analysis acquisition data assetization closed loop transformation degree, and judges whether to carry out closed loop adjustment, finally completes expert random extraction, realizes review matching precision, data transformation efficiency, extraction process is stable and reliable, improve a kind of review expert database random extraction evaluation reliability, solve the low reliability of a kind of review expert database random extraction evaluation in prior art.

The invention provides a named entity recognition method for a high-speed railway technical improvement and overhaul project text, which comprises the following steps of: preprocessing unstructured data and semi-structured data in a picture and / or a text, and converting character-level positions of entity labels by adopting BIOES (Basic Input / Output Element Specification) labeling to obtain a technical improvement and overhaul data set; performing data enhancement on the technical improvement and overhaul data set by adopting a data enhancement strategy to obtain a data enhancement result; and inputting the text data subjected to data enhancement into an RBC fusion model, converting the text data into character vectors, carrying out a sequence labeling task to obtain a prediction label result of each character, and finally realizing an entity of a technical renovation and overhaul text through a large model prompt project method. Through preprocessing, data enhancement and an RBC fusion model, non-structured and semi-structured data in the high-speed rail technical improvement and overhaul project are efficiently identified, the problem that key information cannot be dynamically extracted through a traditional method is solved, and the method has the advantage that the information extraction efficiency and accuracy of the high-speed rail technical improvement and overhaul project are improved.

This invention provides a method and system for constructing a professional knowledge graph for the hot rolling process of strip steel, belonging to the field of industrial monitoring and intelligent manufacturing. The method first acquires multi-source heterogeneous data of the hot rolling process of strip steel, and transforms structured and semi-structured data into unstructured data before merging them into an unstructured dataset; constructs an ontology for the professional knowledge graph, including entity types and entity relationships; divides the unstructured data into training and testing sets; constructs a joint extraction model based on the CasRel framework and multi-hop attention mechanism, and performs training and testing; inputs all unstructured data into the model, identifies and extracts a set of structured triples, processes and stores them to obtain the basic knowledge graph; acquires hot rolling fault data and constructs a fault knowledge graph, and fuses them to obtain the professional knowledge graph for the hot rolling process of strip steel. This invention constructs a professional knowledge graph for the hot rolling process of strip steel, improving the systematic capture capability of hot rolling process data.

The application provides a well logging productivity prediction system and method based on an improved attention mechanism, relates to the field of oil and gas exploration and development, and comprises a semi-structured data conversion module, a depth alignment and resampling module, a training data construction module, a hybrid model construction and training module and an unknown well yield prediction module. The system automatically analyzes WIS logging files, extracts multiple logging curves, realizes depth unification, missing value filling and interactive feature construction; based on a parallel coding structure of a one-dimensional convolution network and a multi-head attention mechanism, the local features and long-range dependencies of the logging curves are jointly modeled by combining a multi-scale residual network, so that high-precision prediction of the oil test yield is realized. The system can generate a depth-yield curve according to the prediction result, divide the yield into five grades, and realize automatic evaluation of the productivity of a new well.

Disclosed is a method and system for creating a product catalogue using a hybrid model. A data reception module receives multimodal data related to a product from one or more data sources that may include structured, unstructured, and semi-structured data. An extraction module extracts text from the product data and is then preprocessed using a preprocessing module, which is further converted into numerical vectors. The hybrid model, an integration of a rule-based model, a Named Entity Recognition (NER) model, and a Generative-AI model, is contextually employed to extract multilingual attributes and values from the text. An attribute-value module generates one or more attribute-value pairs and maps them to the product in a structured format. Finally, a catalogue creation module creates a product catalogue using the one or more attribute-value pairs.

The application provides an electrocardiogram arrhythmia diagnosis training system and a training method, comprising the following steps: S1, during hospitalization or perioperative period, original EEP data is collected by in-vivo collection equipment in an electrocardiogram collection layer, original ECG data is synchronously collected by in-vivo collection equipment, and the two groups of data are transmitted to a data analysis layer; S2, in the data analysis layer, the original EEP data is used to automatically label the corresponding original ECG data, and semi-structured ECG data is formed; S3, the semi-structured ECG data is cut to form structured ECG data, and is sent to a training database; S4, a to-be-trained model is selected, a training key evaluation index is set, an optimal training parameter is saved, and a trained algorithm is output. The system uses the convenience of hospitalization or perioperative period, uses the mode that in-vivo EEP data is automatically labeled into corresponding ECG data to establish a training database, trains an algorithm with massive ECG data with accurate labeling, and thus an algorithm with high accuracy can be obtained.

The application discloses a data storage management system based on a cloud server and particularly relates to the technical field of data storage management, and solves the technical problems that it is difficult to extract unstructured and semi-structured data features to combine structured data features to construct a data type classification model for real-time classification, that there is a lack of value evaluation index values obtained by comprehensively using data frequency, data key ratio and data sensitivity, and that there is a lack of hierarchical storage of various data types according to the value evaluation index values. The system comprises the following modules: a data acquisition module used for deploying various data acquisition units to acquire data; a data classification module used for constructing a classification model; a data processing and analysis module used for analyzing and calculating data frequency, data key ratio and data sensitivity to obtain value evaluation index values; and a data storage and backup module used for storing and distributing data according to the value evaluation index values.

A serialization data format for the transfer, analysis, and modification of schemaless mass data is proposed. The data format supports stream oriented, pipeline-based processing, and it enables read access of contained data without deserialization, and modification access that only requires the deserialization of portions of structure and meta data. Incoming, semi-structured data records may be transformed into records of the proposed serialization data format, compressed and stored in processing buffers containing multiple of those records. During processing, only individual records are decompressed, and processed records are then compressed and stored in output processing buffers for efficient memory usage. Manipulations of data records are performed by appending new values to data records, invalidating old ones and updating access data structures to refer to new values instead of old ones, to enable various modification activities by only requiring append or not-size-changing operations of serialized data records.

The invention relates to the technical field of commercial management, and discloses a foreign trade supplier portrait construction and recommendation method and system based on a large model, and the method comprises the steps: selecting all information data of a supplier based on a data obtaining platform, employing a feature extraction algorithm, converting the behavior data, transaction data, text data and the like of the supplier into multi-dimensional vectors, and carrying out the calculation of the multi-dimensional vectors; a natural language model is utilized to vectorize all information data of selected suppliers to obtain information vectors, a system automatically collects supplier data from different dimensions, and natural language processing and a multi-modal large model technology are utilized to carry out denoising, standardization and semantic association on the data to form unified supplier knowledge representation vectors. The data association degree and the information matching degree in supplier management are improved, so that the system can automatically construct a high-credibility supplier portrait from scattered public and semi-structured data, the manual input and auditing cost is reduced, and the comprehensiveness and timeliness of portrait information are improved.

The invention discloses an LLM-based measurement and detection business consultation method and related equipment, and the method comprises the steps: obtaining a measurement and detection document, carrying out the analysis and recognition, extracting document elements, and converting the document elements into semi-structured data; the method comprises the following steps: performing fine tuning on a pre-trained large language model and an embedded model based on measurement detection field data to correspondingly obtain a field-specific large model and a field-specific embedded model, performing semantic analysis on semi-structured data, and performing semantic similarity-based dynamic partitioning on the semi-structured data by using the field-specific embedded model to obtain a field-specific large model and a field-specific embedded model; converting the text blocks into vector data and storing the vector data into a vector knowledge base; and receiving a consultation question of a target object, performing multi-path semantic retrieval in the vector knowledge base, obtaining context information, inputting the consultation question and the context information into the field-specific large model together, and generating a natural language consultation answer. The method can effectively adapt to complex business scenes and can be widely applied to the technical field of data processing.

The invention provides a BIM-based component-level knowledge graph construction method and system, and the method comprises the steps: loading a preset export plug-in, and exporting the component data of each component in a building information model in the form of a semi-structured data format file; analyzing the exported file by adopting an analysis tool to extract component data, and storing the component data into a component data structure; for hierarchical path parameters in each component data structure, generating a knowledge graph entity and mapping a front node name and component data into entity attributes; searching a post node of each node in a recursive traversal mode to obtain a hierarchical relationship between the components; and based on the hierarchical relationship, the knowledge graph entity and the corresponding entity attribute, generating a knowledge graph corresponding to the building information model. According to the method, the problems of low construction efficiency of the domain knowledge graph of the railway building information model and insufficient semantic relationship mining can be solved; and the construction efficiency and the information accuracy of the knowledge graph are improved.

This application provides a method, apparatus, electronic device, and storage medium for converting semi-structured data. The method includes: acquiring semi-structured data; parsing fields of objects in the semi-structured data to obtain a base table; obtaining a derived table based on the base table; constructing a standard template document based on the base table and the derived table; and inputting the semi-structured data into the standard template document for conversion to obtain structured data. Implementing this application embodiment enables rapid conversion of semi-structured data, allowing it to realize its shared value, reducing the work costs of repeated communication and rereading development during data conversion, and greatly improving the conversion efficiency of semi-structured data.

Various embodiments of the teachings herein include a method for creating a knowledge graph in the industrial field. An example includes: obtaining unstructured data from a first source in a sub-field of the industrial field, with knowledge annotations; performing machine learning on the unstructured data to generate a first model adapted to extract knowledge; extracting knowledge from second unstructured data provided by the first source based on the first model, without knowledge annotations; obtaining first structured data and first semi-structured data from a second source in a second sub-field; extracting second knowledge from the first structured data; extracting third knowledge from the first semi-structured data; and building a knowledge graph integrating the first and second sub-field based on the first, second, and third knowledge, represented in the form of triples.

The invention discloses a video diagnosis method and device, a video monitoring platform and a storage medium. The method comprises the following steps: in response to triggering of a video diagnosis event, traversing video frames of video streams of all cameras in a lowest-level domain in a video monitoring platform, and converting the video frames of all the cameras into initial semi-structured data; the lowest-level domain encodes all the initial semi-structured data to generate a semi-structured code stream, and transmits the semi-structured code stream to a top-level domain of the video monitoring platform through an upper-level domain of the lowest-level domain according to a sequence of domain levels from low to high; and the top-level domain transmits the semi-structured code streams to a video diagnosis server until the semi-structured code streams reach a preset number, so that the video diagnosis server performs video diagnosis on the video streams of the cameras based on the preset number of semi-structured code streams. According to the scheme, the video diagnosis efficiency and accuracy of each camera can be effectively improved.

The invention relates to the technical field of financial service data processing, and discloses a financial big data-oriented fusion analysis method, which comprises the following steps: establishing a financial service data platform, collecting structured data, semi-structured data and unstructured data, and preprocessing and storing data of different formats and different frequencies through an ETL (Extract Transform and Load) process; and searching the investment project of which the transaction volume is obviously changed in the structured data and the semi-structured data, and marking the investment project as a monitoring project. According to the method, structured, semi-structured and non-structured data are integrated, so that the data collection breadth is expanded fundamentally, early warning information which is not displayed in financial transaction data at ordinary times is captured, the cross-domain data fusion mode reduces the influence of single data source noise on risk estimation, and the risk estimation accuracy is improved. Therefore, normal market fluctuation and real black swan events can be distinguished more accurately in the face of sudden risks.

To provide a more convenient program etc. for managing tabular data and a layout that defines the relationship between each element of the tabular data. The program saves tabular data with a given version and a layout that defines the relationships between elements of the tabular data, and when an update to the tabular data is received, causes a computer to execute a process of saving the updated tabular data and layout in association with the new version.Semi-structured data can be output based on the tabular data and layout.